Abstract:

As a serious systemic infection, the prevalence of bloodstream infection has gradually increased in recent years, which is one of the main causes of poor prognosis of patients, so it is particularly important to identify high-risk patients with poor prognosis early and timely. However, the traditional statistical analysis of bloodstream infection prognosis prediction can not meet the clinical needs in terms of reliability and validity, and since machine learning algorithms have achieved good application results in the construction of prediction models for some clinical problems, showing their application prospects to improve the accuracy of clinical diagnosis and treatment, this paper mainly reviews the application status of decision tree algorithm in the prognosis prediction of bloodstream infection, and prospects its application in the prediction of bloodstream infection prognosis by comparing its advantages and disadvantages with traditional methods. This review aims to explore better predictive methods for early clinical identification of high-risk patients and minimize the mortality rate of bloodstream infections.

Key words: Artificial intelligence, Machine learning, Decision tree, Bloodstream infection, Prognosis evaluation